Cellulose nanocrystals reinforced shape memory polymer cardiovascular stent
Bibliographic record
Abstract
Purpose The purpose of this paper is to demonstrate an innovative, fast and low-cost method to fabricate customized stents using polyurethane-based shape memory polymers composite reinforced by cellulose nanocrystal (CNC), achieved by a commercial desktop extrusion-based additive manufacturing (EBAM) device. Design/methodology/approach The composite filament for printing the stents was prepared by a two-step melt-compounding extrusion process. Afterward, the stents were produced by a desktop EBAM printer. Thermal characterizations, including thermo-gravimetric analysis (TGA) and modulated differential scanning calorimetry (modulated DSC), were conducted on stent samples and filament samples, respectively. Then the stents were programmed under 45°C. Recovery characterizations, including recovery force and recovery ratio measurement, were conducted under 40°C. Findings TGA results showed that the materials were stable under the printing temperature. Modulated DSC results indicated that, with the addition of CNCs, the glass transition temperature of the material dropped slightly from 39.7°C at 0 Wt.% CNC to 34.2°C at 7 Wt.% CNC. The recovery characterization showed that the stents can exert a maximum recovery force of 0.4 N/mm when 7 Wt.% of CNCs were added and the maximum recovery ratio of 35.8% ± 5.1% was found when 4 Wt.% of CNCs were added. The addition of CNC improved both the recovery ratio and the recovery force of the as-prepared stents. Originality/value In terms of recovery force, the as-prepared stents out-performed commercially available stents by 30 times. In addition, additive manufacturing offers more flexibility in the design and fabrication of customized cardiovascular stents.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".